F-1
Evidence Table Codebook

Each research study included in the systematic evidence review was coded on several descriptive and evaluative dimensions. Text description and commentary was also provided. This Codebook was used by each coder to ensure consistency in the information and judgments entered into the Evidence Table. Anything in bold should be entered exactly as described so that information on that variable can be sorted later.

Reference: Author Name(s)

Enter the last name(s) of the author(s) in order. If two authors, enter both last names separated by a comma. If more than two authors, use “et al.” after the last name of the first author.

Reference: Year

Enter the publication year; use all four digits for the year.

Link Number (#)

Enter the number of the link (relationship) that is being studied: 1 if the relationship is between marketing and a precursor (mediator) to diet, 3 if the relationship is between marketing and diet, and 5 if the relationship is between marketing and diet-related health. If the study has information or sub-studies about more than one link, make each link a separate line in the Evidence Table.

For studies in which television viewing was measured and interpreted as an indicator of exposure to televised advertising, add TV after the link number.



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Food Marketing to Children and Youth: Threat or Opportunity? F-1 Evidence Table Codebook Each research study included in the systematic evidence review was coded on several descriptive and evaluative dimensions. Text description and commentary was also provided. This Codebook was used by each coder to ensure consistency in the information and judgments entered into the Evidence Table. Anything in bold should be entered exactly as described so that information on that variable can be sorted later. Reference: Author Name(s) Enter the last name(s) of the author(s) in order. If two authors, enter both last names separated by a comma. If more than two authors, use “et al.” after the last name of the first author. Reference: Year Enter the publication year; use all four digits for the year. Link Number (#) Enter the number of the link (relationship) that is being studied: 1 if the relationship is between marketing and a precursor (mediator) to diet, 3 if the relationship is between marketing and diet, and 5 if the relationship is between marketing and diet-related health. If the study has information or sub-studies about more than one link, make each link a separate line in the Evidence Table. For studies in which television viewing was measured and interpreted as an indicator of exposure to televised advertising, add TV after the link number.

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Food Marketing to Children and Youth: Threat or Opportunity? Link? Y/N Enter Y (yes) if the link is significant at p equal to or less than .05; enter N (no) otherwise. For studies with statistical tests of more than one measure of the cause and/or effect or with statistical tests of various subgroups (e.g., boys and girls)—all for the same link—enter Y if any of the tests were significant and describe them all in the abstract. If none are significant, enter N. Research Method Enter one of the following six abbreviations (in parentheses). See the definitions that follow the terms. Natural experiment (Exp-N) Randomized trial (Exp) Panel (L-Pnl) Cohort (L-Coh) Trend (L-Trnd) Cross-sectional (CS) Experimental Studies Natural experiment (Exp-N): Treatment assigned serendipitously but randomly. For example, in the early 1990s in the Milwaukee School Voucher program, there were more students who applied for school vouchers than vouchers available. All applicants were entered in a lottery, with only the winners getting vouchers. Randomized trial (Exp): Treatment assigned deliberately and randomly. Nonexperimental (Observational) Studies Longitudinal Studies: Panel (L-Pnl): Measures the same sample of individuals at different points in time. Cohort (L-Coh): Similar subjects (age, demographics, etc.) are followed over time and compared on outcome or descriptive measures (e.g., health). Cohort studies typically involve a sample in which some individuals have a property and some do not (e.g., smokers versus nonsmokers). Trend (L-Trnd): Samples different groups of people at different points in time from the same population, using the same measures. Cross-Sectional Studies: Cross-sectional (CS): Nonexperimental study at a single point in time. Cause Variable Briefly describe the marketing variable considered the causal (initiating,

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Food Marketing to Children and Youth: Threat or Opportunity? independent) variable in the research. To describe it, use short, simple terms such as “vending machines in school,” “exposure to food ads,” and “television viewing.” If there are multiple cause variables, all for the same link number, describe them all. Do not create separate lines for each variable. If some are significant and some are not (again all testing the same link in the model), enter Y in the link-significant column and describe all of the p-values for all the cause variables (testing the same link) in the mini-abstract. Do not describe the specifics of how the variable was measured. In this column, to the extent possible, use a very short, general descriptor, closely tied to (if not the same as) one of the terms in the initiating variable box for the link in the Conceptual Framework that is being studied. Cause Variable Measure Describe the way(s) in which the independent variable was measured (for a nonexperimental study) or implemented in treatment conditions (for experimental study). Do so only for the link identified in this line of the Evidence Table. As examples, measurement techniques could be “self-report questionnaire,” “parent interview,” “sound-activated videotaping in rooms with television sets,” or “ads taken from cable stations and inserted into cartoons taken from similar cable stations.” Cause Variable Category Based on a description of the cause variable and how it was measured, determine which of the following possible categories best describes it. If more than one cause variable (with the same link) or more than one measure of the same cause were used, choose the best description for each. TV ads: Experiment TV ads: Viewing only TV ads: Observed in natural setting TV ads: Viewing + other media TV ads: Campaign Product placement in film Print ads Radio ads Multimedia campaign Price and promotion Other Effect Variable Briefly describe the variable considered the effect (consequent, dependent) variable in the research. It will be a precursor, diet, or diet-related health variable. To describe it, use simple terms such as “food preferences,” “belief food is good for you,” “increased drinking of Pepsi.” If there are multiple effect variables, all for the same link number, describe them all. Do

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Food Marketing to Children and Youth: Threat or Opportunity? not create separate lines for each variable. If some are significant and some are not (again all testing the same link in the model), enter Y in the link significant column and describe all of the p-values for all the effect variables (testing the same link) in the mini-abstract. In this column, don’t describe the specifics of how the variable was measured. In this column, to the extent possible, use a very short, general descriptor, closely tied to (if not the same as) one of the terms in the box for the link in the Conceptual Framework that is being studied. Effect Variable Measure Describe the way(s) in which the dependent variable was measured. Describe all measures for every variable treated as a dependent variable for the link identified in this line of the evidence table. As examples, measurement techniques could be “self-report questionnaire,” “parent interview,” “observations at school cafeteria,” “total sales,” or “body mass index (BMI) calculated from weight and height measured by health professional.” Effect Variable Category Based on a description of the effect variable and how it was measured, determine which of the following possible categories best describes it. If more than one effect variable (with the same link) or more than one measure of the same effect were used, choose the best description for each. In parentheses is the link number for which each effect variable term can be used. Preferences (Link 1) Requests (Link 1) Beliefs (Link 1) Short-term consumption (Link 3) Usual diet (Link 3) Adiposity (Link 5) Other (Links 1, 3, 5) Sample Size Enter the sample size as a number. If the number is 1,000 or greater, use a comma. Choose the number that is included in the analyses not the number the researcher started with, if these numbers are different. If there is significant participant loss, note that in the “Other Comments” column. If more than one sample was studied, make each sample a separate line. In the mini-abstract, if useful, include more information about the sample; for example, include text description and numbers for different sectors of the sample (e.g., 100 children/teens and 100 parents, one parent for each child/teen). You might also decide to mention participant loss/attrition here.

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Food Marketing to Children and Youth: Threat or Opportunity? Sample Age Enter IT, YC, OC, or T if the sample is respectively all infants and toddlers (under 2 years of age), younger children (2–5 years), older children (6–11 years), or teens (12–18 years). If the sample is largely just one of these groups, enter that age group only or enter where the mean of the group falls (e.g., “mean 5.7,” “preschool and some kindergarten,” and “age 4 plus/minus 2 years” are all entered as YC). If the sample is described as more than one of these groups, enter the relevant letters, following the IT, YC, OC, T order (e.g., “OCT” not “TOC” for a sample of teenagers and older children). Do not use commas to separate the letters. If the sample was tested more than once, enter the age group for the sample when first tested. If at the later testing(s), the sample has grown into an older age group, add that. If still in the same age group, add nothing. If the article has no information about the age of the participants, enter No Info. Indicate in the mini-abstract if the sample age clusters in one part of the indicated age range (e.g., if the sample of “older children” is only 6- and 7-year-olds, indicate this in the mini-abstract). Note that it is not uncommon to see a child’s age described as “3-11” or “6-1” to denote that the child was 3 years, 11 months, or 6 years, 1 month, respectively. When read in context, the reviewer should be able to figure out whether “3-11” means 3 years, 11 months, or 3 to 11 years. Measure Quality Enter H, M, or L to indicate high, medium, or low, following the guidance below. In the “Other Comments” column, include a brief description of the rationale for your rating choice. In general, the quality of the measure of the independent/cause variable is more important than the quality of the measure of the dependent/effect variable in determining this rating. Measurement of the control variables in a non-experimental study is also important. Finally, if any self-report measure is used, the best rating possible is M for overall measurement quality. There are three primary criteria for evaluating the quality of measures in a study: validity, reliability, and precision, each of which is explained in more detail below. Validity refers to the extent to which an instrument directly and accurately measures what it is intended to measure. Reliability assesses the extent to which the same measurement technique, applied repeatedly, is likely to yield the same results. Precision refers to the fineness or coarseness of a measure. Validity Validity refers to the extent to which an operationalized measure di-

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Food Marketing to Children and Youth: Threat or Opportunity? rectly and accurately measures the concept it is intended to measure. In measuring a child’s food preferences in response to an ad, for example, a measure in which a child’s actual food consumption was recorded is more valid than a measure in which a child’s self-report of his/her future intentions was recorded. The total amount of TV watched is often used as a measure of the total exposure to advertising, but such a measure is lower in validity than is a direct measure of advertising exposure. For example, some people might channel surf or do chores during ads or they might watch programs with ads that are unlikely to be relevant to children. Bias affects validity, but certain kinds of bias are much worse than others. A highly reliable bathroom scale that is set 7 pounds light is biased, but its bias is constant, the scale measures what it is supposed to, and thus the scale still has high validity, especially when measuring change in weight over time. Self-reports of socially undesirable behaviors are usually biased, and the amount of bias usually varies with the amount of social undesirability (e.g., heavy candy eaters are likely to underreport more severely than are those who eat less candy). Reliability Reliability assesses the extent to which the same measurement technique, applied repeatedly, is likely to yield the same results when it is believed that the variable measured has not changed. Measuring a child’s weight with a good bathroom scale is highly reliable, but measuring the child’s cumulative exposure to environmental lead since birth by measuring the concentration of lead in drawn blood is not reliable (it varies greatly from day to day). Almost no one can remember every bit of food he or she consumed last week, so measures that depend on recall are typically low in reliability (and also typically low on validity). Reliability is often important in studies involving subjective coding of observed behavior. One, we want the same coder to score the same behavior in the same way across times (intracoder reliability), and two, we want different coders to score the same behavior in the same way (intercoder reliability). Precision Precision refers to the fineness or coarseness of a measure. For example, recording family income as low, medium, or high is less precise than recording the number of dollars in family income, such as $18,500. Overall Scoring for Measure Quality Studies that rank high on all three factors—high on validity AND on

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Food Marketing to Children and Youth: Threat or Opportunity? reliability AND on precision—would be considered to have H, high measure quality. Studies that rank high on one or two of the three factors and low on none would be considered to have M, medium measure quality. Studies that do not rank high on any of the three factors or that rank low on at least one would be considered to have L, low measurement quality. Causality Evidence Enter H, M, or L to indicate high, medium, or low, following the guidance below. In the “Other Comments” column, include a brief description of the rationale for your rating choice. The rating of causality evidence is entirely separate from that for ecological validity. The idea is to rate the quality of the case that can be made for interpreting a statistically significant association as causal and not just an association. We separate experimental studies from observational studies in explaining how to do the rating. Experimental Studies 1. Treatment Bias The essential feature of an experimental study that allows causal inference is that assignment of treatment be independent of any potential confounder, that is, any property that might also have an influence on the outcome. When treatment is assigned randomly, treatment bias is (at least in theory) eliminated. In natural experiments, or experiments without randomized assignment of treatment, treatment bias is a real concern. For example, an experiment comparing an online course on computer programming to a human-taught course on the same topic in which participants were allowed to choose the condition would obviously suffer from treatment bias, as those with high computing aptitude and/or experience are more likely to choose the online condition. The antidote to treatment bias is analogous to what is required in observational studies: we must measure and statistically control for the confounding property, perhaps with a pretest. For example, if the online/ human study measured computer aptitude prior to the course and then controlled for it, treatment bias would not be a large concern. 2. Dropout/Attrition Bias Experiments in which dropout during the trial is associated with a potential confounder are suspect for causal inference. For example, in the Milwaukee School Voucher evaluation “natural experiment,” in which all participants wanted vouchers but only those who won a lottery received them, both groups experienced fairly high dropout rates between the enroll-

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Food Marketing to Children and Youth: Threat or Opportunity? ment period and the post-test. Researchers were concerned that, among the students who did not win the lottery, those who dropped out were the ones with aggressive concerned parents, leaving behind those likely to learn less. 3. Measurement Studies in which the measures have low validity are suspect for causal inference. For example, an experiment in which the cause of interest was advertising exposure—but only overall TV watching was experimentally manipulated—suffers from this problem because overall TV exposure is a weak measure of advertising exposure. 4. Experimental Studies—Summary Experimental studies with no treatment bias, no dropout bias, and reasonable validity in measurement should be given H, High Causality Evidence. Studies with serious treatment bias should be given L, Low Causality Evidence. Observational Studies In an observational study, an association between a putative cause X and an effect Y might be due to any combination of (1) X is a cause of Y, (2) some third factor is a common cause of both, or (3) Y is a cause of X. The overall assessment of causal validity rides on how convincingly the study eliminates possibilities 2 and 3. 1. Time One common strategy is to use time order to eliminate possibility 3. The fact that X is measured prior to Y, however, does nothing to eliminate possibility 2, that there is a confounder that occurs prior to both X and Y that is responsible for both X and Y and accounts for the apparent association between X and Y. 2. Confounders/Controls/Omitted Variables/Covariates 2a. Inclusion The most common obstacle to causal inference in observational studies is the possibility that the statistical association between the putative cause and effect might be spurious, that is, due to an omitted variable that is a cause of both. Such factors are commonly referred to as confounders, covariates, third variables, omitted variables, etc. To be rated H, High on causal inference validity, an observational study must include—that is, measure and statistically control for—all confounders that are significantly associated with both the cause and effect. A study that controls for most significant but not all possible confounders can still be rated M, Medium on causal inference validity.

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Food Marketing to Children and Youth: Threat or Opportunity? 2b. Measures Measurement is crucial to causal inference in observational studies, but not in a simple way. If any of the measures lacks validity, then the case for causality is weakened. Although a lack of precision or reliability in the dependent variable will affect the statistical inference (the standard errors and p-values), it will not bias the coefficient estimate and is of little or no consequence to the causal inference validity. When the cause, or independent, variable is measured with low reliability, then the estimate of its effect is biased toward zero, making it harder to find a significant p-value, and thus in some sense strengthening the case for causation. When a covariate, that is, a variable being “controlled for” in the analysis, is measured with low reliability or precision, however, the estimate of the association between cause and effect will be biased.1 In some cases the bias will be toward zero, and in others away from zero. The direction of the bias is the same as the case in which the variable is entirely left out of the analysis. 3. Observational Studies—Summary An observational study should score H, High on Causality Evidence when (a) the possibility that the response variable is a cause of the independent variable can be eliminated (perhaps by time), (b) the cause and effect are measured with high validity, and (3) all significant confounders have been included and measured with high validity, reliability, and precision. Ecological Validity Enter H, M, or L to indicate high, medium, or low, following guidance below. In the “Other Comments” column, include a brief description of the rationale for your rating choice. Ecological validity refers to the extent to which an investigation’s research setting, stimuli, and response demands are similar to those of the naturally occurring settings, stimuli, and responses characteristic of the behavioral system being studied. H, high ecological validity, occurs when the research setting and stimuli/cause and effect/response are similar to those of the system under investigation. 1   The sign of the bias is the same as the bias that would result if the variable was omitted from the analysis entirely, and the size of the bias is proportional to the size of the measurement problem. A covariate with a highly imprecise measure will result in more bias than one with a mildly imprecise measure.

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Food Marketing to Children and Youth: Threat or Opportunity? An example would be an experiment that manipulated the signage on the vending machines in schools with the measured responses being the purchases made from the vending machines. Another example would be a survey in which parents and children reported on the children’s home television viewing and daily diet. M, medium ecological validity, occurs when the research setting or the stimuli or the responses are similar to those of the system under investigation. An example would be an experiment in which children are brought to a university laboratory, shown a children’s TV program with embedded food commercials, and then allowed to choose food from a selection of food items. The foods chosen and the amounts eaten are the responses. In this case, the setting is not a natural setting for the child, but the stimuli (children’s TV program and commercials) and the responses are at least moderately similar to natural stimuli and responses. L, low ecological validity, occurs when neither the research setting nor the stimuli nor the responses are similar to those of the system under investigation. An example would be an experiment in which a child is brought to a university laboratory, hears a description of a television commercial, explains the intent underlying the broadcast of the commercial, and chooses a food from several pictures of food. In this case neither the setting, nor the stimuli, nor the response demands would be characteristic of the behavioral system under investigation. Mini-Abstract Provide a brief description of the main elements of the study. If the research was conducted outside the United States, indicate where it was conducted. Lead Reviewer Enter the last name of the person designated as lead reviewer. Other Comments: Lead Reviewer The lead reviewer adds any comments about unusual results or features of the research, questions he or she had in reviewing the research or filling in the Evidence Table, opinions about the overall quality of the work, arguments for why the apparently positive causal relationship should be discounted, and other information that seems pertinent. Also be sure to include the rationales for the Measure Quality, Causality Evidence, and Ecological Validity ratings. Second Reviewer Enter the last name of the person designated as the second reviewer.

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Food Marketing to Children and Youth: Threat or Opportunity? Other Comments: Second Reviewer The second reviewer adds any comments about unusual results or features of the research, questions he or she had in reviewing the research or filling in the Evidence Table, opinions about the overall quality of the work, arguments for why the apparently positive causal relationship should be discounted, and other information that seems pertinent. Also be sure to include the rationales for the Measure Quality, Causality Evidence, and Ecological Validity ratings.

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Food Marketing to Children and Youth: Threat or Opportunity? Cause Variable Category Effect Variable Category Sample Measure Quality Causality Evidence Ecological Validity Size Age TV ads—Viewing only Adiposity 1,651 OCT M M H TV ads—Viewing only Adiposity 1,020 YC L L H Print ads Beliefs, preferences, and requests 36 YC M M L TV ads—Experiment Requests 40 YCOC L M L TV ads—Viewing only Adiposity 112 OCT L L H TV ads—Viewing only Adiposity 112 OCT L L H TV ads—Viewing + other media Adiposity 198 OC L L H TV ads—Viewing only Usual diet 214 OC M L H TV ads—Viewing + other media Adiposity 2,389 OCT L L H TV ads—Experiment Preferences 363 YCOC M H L TV ads—Viewing only Requests 185 T M L H TV ads—Viewing only Usual diet 1,497 YCOC M L H TV ads—Viewing only Adiposity 1,497 YCOC M L H Price and promotion Preferences 35 OCT M L M TV ads—Viewing + other media Adiposity 2,379 OC M M H TV ads—Viewing only Adiposity 2,634 OC L M H TV ads—Viewing only Beliefs 64 YC M M L TV ads—Viewing only Adiposity 2,372 OC M L M TV ads—Experiment Beliefs and preferences 106 OC M M M TV ads—Experiment Short-term consumption 106 OC H M M TV ads—Campaign Preferences 75 YC M M M TV ads—Viewing only Adiposity 106 YCOC M M H

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Food Marketing to Children and Youth: Threat or Opportunity? Reference Relationship Significance Research Method Author(s) Year Reid et al. 1980 Precursors (TV) N CS Reilly et al. 2005 Health (TV) Y L-Pnl Resnik, Stern 1977 Precursors Y Exp Ritchey, Olson 1983 Precursors (TV) N CS Ritchey, Olson 1983 Diet (TV) Y CS Robinson 1999 Diet (TV) N Exp Robinson 1999 Health (TV) Y Exp Robinson et al. 1993 Health (TV) N CS Robinson et al. 1993 Health (TV) N L-Pnl Robinson, Killen 1995 Diet (TV) Y CS Robinson, Killen 1995 Health (TV) N CS Ross et al. 1981 Precursors N Exp Ross et al. 1981 Precursors Y Exp Shannon et al. 1991 Health (TV) Y L-Pnl Shannon et al. 1991 Health (TV) N CS Sherwood et al. 2003 Health (TV) N CS Signorielli, Lears 1992 Precursors (TV) Y CS Signorielli, Lears 1992 Diet (TV) Y CS Signorielli, Staples 1997 Precursors (TV) Y CS Stettler et al. 2004 Health (TV) Y CS Stoneman, Brody 1981 Precursors Y Exp Stoneman, Brody 1982 Precursors Y Exp

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Food Marketing to Children and Youth: Threat or Opportunity? Cause Variable Category Effect Variable Category Sample Measure Quality Causality Evidence Ecological Validity Size Age TV ads—Viewing only Beliefs 138 OC L L M TV ads—Viewing only Adiposity 5,493 YCOC L M H TV ads—Experiment Preferences 45 OC H H L TV ads—Viewing only Preferences 122 YC M L H TV ads—Viewing only Usual diet 122 YC L M M TV ads—Viewing only Usual diet 192 OC M H H TV ads—Viewing only Adiposity 192 OC M H H TV ads—Viewing only Adiposity 671 T L L M TV ads—Viewing only Adiposity 279 T L M M TV ads—Viewing + other media Usual diet 1,912 T L L H TV ads—Viewing + other media Adiposity 1,912 T L L H TV ads—Experiment Beliefs 100 YCOC L M M TV ads—Experiment Beliefs 100 YCOC L M L TV ads—Viewing only Adiposity 489 OC M L H TV ads—Viewing only Adiposity 773 OC M L H TV ads—Viewing + other media Adiposity 96 OC L L H TV ads—Viewing only Beliefs 209 OC M M H TV ads—Viewing only Usual diet 209 OC M M H TV ads—Viewing only Beliefs and preferences 427 OC M M H TV ads—Viewing only Adiposity 872 OC M M H TV ads—Experiment Preferences 124 OC M H L TV ads—Experiment Requests 36 YC H H M

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Food Marketing to Children and Youth: Threat or Opportunity? Reference Relationship Significance Research Method Author(s) Year Storey et al. 2003 Health (TV) Y CS Storey et al. 2003 Health (TV) Y CS Sugimori et al. 2004 Health (TV) Y L-Pnl Tanasescu et al. 2000 Health (TV) Y CS Taras et al. 1989 Diet (TV) Y CS Taras et al. 2000 Diet Y CS Taras et al. 2000 Precursors Y CS Toyran et al. 2002 Precursors Y CS Toyran et al. 2002 Health (TV) Y CS Tremblay, Willms 2003 Health (TV) Y CS Trost et al. 2001 Health (TV) N CS Tucker 1986 Health (TV) N CS Utter et al. 2003 Diet (TV) Y CS Utter et al. 2003 Health (TV) Y CS Vandewater et al. 2004 Health (TV) N CS Wake et al. 2003 Health (TV) Y CS Waller et al. 2003 Health (TV) N CS Wolf et al. 1993 Health (TV) Y CS Wong et al. 1992 Health (TV) Y CS Woodward et al. 1997 Diet (TV) Y CS Yavas, Abdul-Gader 1993 Precursors Y CS Zive et al. 1998 Diet (TV) Y CS

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Food Marketing to Children and Youth: Threat or Opportunity? Cause Variable Category Effect Variable Category Sample Measure Quality Causality Evidence Ecological Validity Size Age TV ads—Viewing only Adiposity 3,473 OCT M M H TV ads—Viewing only Adiposity 8,772 OCT M M H TV ads—Viewing only Adiposity 8,170 YCOC L L H TV ads—Viewing only Adiposity 53 OC L L H TV ads—Viewing only Usual diet 66 YCOC L L H TV ads—Campaign Usual diet 237 YC M L H TV ads—Campaign Requests 237 YC L L H TV ads—Viewing only Requests 886 OC L L H TV ads—Viewing only Adiposity 886 OC L L H TV ads—Viewing only Adiposity 7,216 OC L L H TV ads—Viewing + other media Adiposity 187 OC L L H TV ads—Viewing only Adiposity 379 T M L H TV ads—Viewing + other media Usual diet 4,480 T M M M TV ads—Viewing + other media Adiposity 4,480 T M L M TV ads—Viewing only Adiposity 2,831 ITYCOC M M H TV ads—Viewing only Adiposity 2,862 OCT L L H TV ads—Viewing only Adiposity 880 OC L L H TV ads—Viewing only Adiposity 552 OCT L L H TV ads—Viewing only Other 1,081 YCOCT M M H TV ads—Viewing only Usual diet 2,082 T L L H TV ads—Viewing only Requests 217 OC M L H TV ads—Viewing only Usual diet 351 YC M L H

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